使用pytorch生成一段代码用于碳交易价格预测，其中包含lstm模型，输入的特征有5个

你好，关于使用 PyTorch 生成一段代码用于碳交易价格预测，其中包含 LSTM 模型，输入的特征有 5 个，我可以给你提供以下的代码示例：

import torch
import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h = torch.zeros(self.num_layers, x.size(), self.hidden_size).to(device)
        c = torch.zeros(self.num_layers, x.size(), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h, c))
        out = self.fc(out[:, -1, :])
        return out

input_size = 5
hidden_size = 64
num_layers = 2
output_size = 1

model = LSTMModel(input_size, hidden_size, num_layers, output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=.001)

# train the model
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)

        # forward pass
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        # backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# predict
with torch.no_grad():
    inputs = test_data.to(device)
    outputs = model(inputs)
    predicted = scaler.inverse_transform(outputs.cpu().numpy())

以上是一个简单的 LSTM 模型，其中包含了 5 个输入特征，可以用于碳交易价格的预测。需要注意的是，这只是一个示例代码，具体的实现需要根据具体的数据集和需求进行调整和优化。